Photon-counting CT (PCD-CT), compared to conventional CT, offers higher spatial resolution, lower radiation dose, reduced use of iodine contrast agents, and superior image quality, and it has gradually been introduced into clinical practice. PCD-CT demonstrates advantages across various body systems, particularly in the detailed imaging of coronary artery stenosis, plaque composition, and stents. By reviewing foreign literature and current domestic exploration of PCD-CT applications, the prospects for its clinical research and application in China are discussed.

Objective To explore the diagnostic performance of a spectral CT-based radiomics machine learning model and nomogram for preoperatively identifying the KRAS gene status in patients with colorectal cancer (CRC). Methods A total of 137 CRC patients who underwent KRAS mutation detection and preoperative spectral CT examination were retrospectively included (70 cases with KRAS wild type and 67 cases with KRAS mutant type). They were randomly divided into a training set (95 cases) and a test set (42 cases) in a 7∶3 ratio. Tumor region of interest (ROI) was delineated on venous-phase 70 keV monochromatic enhanced CT images, and radiomics features were extracted and selected. A radiomics score (Rad-score) was calculated using least absolute shrinkage and selection operator (LASSO) regression. Six models were established including three radiomics models based on support vector machine (SVM), extreme gradient boosting (XGBoost), and logistic regression (LR), as well as three combined models integrating spectral CT imaging features with the Rad-score. Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and compared using the Delong test. A radiomics nomogram was constructed based on the Rad-score and validated in the test set. Calibration curves, decision curve analysis (DCA), and clinical impact curves were used to assess calibration, clinical net benefit, and clinical utility. Results A total of 8 radiomics features and 1 spectral parameter were selected. In the test set, the LR-based combined model demonstrated the best performance, with an AUC of 0.891, outperforming the combined models based on SVM (AUC=0.796), XGBoost (AUC=0.787), and LR (AUC=0.812) (all P<0.05), as well as the combined models based on SVM (AUC=0.889) and XGBoost (AUC=0.873) (both P<0.05). The nomogram model achieved AUCs of 0.987 and 0.916 in the training and test sets, respectively. The calibration curve showed good agreement in the training set, while performance in the test set was slightly lower. DCA and clinical impact curves demonstrated that the nomogram provided favorable clinical net benefit and utility. Conclusion The LR-based model and nomogram, constructed using venous-phase spectral CT and radiomics features, offer valuable preoperative insights into KRAS gene status in CRC patients and may serve as a reference for clinical decision-making.

Amyloid-related imaging abnormalities (ARIA) are among the most common adverse reactions during Aβ monoclonal antibody treatment for early Alzheimer’s disease (AD). Magnetic resonance imaging (MRI) serves as a crucial tool tool for monitoring the occurrence of ARIA and assessing its severity throughout the treatment process. This paper provides a detailed overview of the mechanisms of ARIA, its imaging manifestations, and severity grading. Additionally, a comprehensive standard MRI examination protocol and monitoring workflow are proposed based on clinical practice experience. The study also highlights the critical role of imaging monitoring in guiding clinical medication for AD patients.

Ultrafast dynamic contrast-enhanced (UF-DCE) MRI, with its advantages of high imaging speed, high temporal resolution, and the ability to obtain rich hemodynamic parameters, has been utilized in the early screening, differential diagnosis, neoadjuvant chemotherapy efficacy prediction, and prognostic evaluation of breast cancer. This review summarizes the technical principles of UF-DCE MRI, its applications in breast cancer diagnosis and treatment, and the research progress on artificial intelligence applications in UF-DCE MRI.

Habitat imaging(HI) can analyze tumor heterogeneity and microenvironmental characteristics and has been increasingly applied in the research, diagnosis, and treatment of common digestive system tumors, including colorectal cancer, gastric cancer, and hepatocellular carcinoma. Currently, HI is used to construct genotypic prediction models, precision staging, and metastasis prediction in colorectal cancer; to quantify immune microenvironment characteristics, evaluate treatment response, and predict prognosis in gastric cancer; and to achieve non-invasive identification of microvascular invasion and recurrence risk stratification in hepatocellular carcinoma. This article introduces the basic principles and technical processes of HI, and reviews its research progress in the above-mentioned digestive system tumors.

In 2024, the American Heart Association (AHA) issued a scientific statement on the diagnosis and management of cardiac sarcoidosis (CS), systematically outlining the diagnostic and therapeutic framework for this infiltrative cardiomyopathy characterized by non-necrotizing granulomatous inflammation. The Statement emphasizes that multimodal imaging is a core pillar of CS diagnosis. This article focuses on the core content of the statement, specifically interpreting the clinical application value and diagnostic standards of multimodal imaging techniques for CS, as well as the collaborative diagnostic and therapeutic strategies involving cardiac magnetic resonance (CMR) and positron emission tomography (PET), with the aim of providing precise imaging assessment pathways for clinical practice.

Radiomics is a technique that extracts quantitative information from medical images for characterization and analysis, providing supplementary information for the diagnosis and treatment of clinical diseases. Feature selection plays a crucial role in radiomics by enhancing the accuracy and predictive performance of machine learning models. This paper reviews the classification, advantages, and disadvantages of feature selection methods in radiomics, their applications, and factors that influence the accuracy and stability of feature selection.

Objective To investigate the association between the pericoronary adipose tissue fat attenuation index (FAI) surrounding culprit plaques in acute coronary syndrome (ACS) and plaque characteristics, and to assess its value in predicting culprit plaques. Methods This retrospective study enrolled 50 patients diagnosed with ACS (ACS group) and 40 asymptomatic individuals with coronary atherosclerosis who underwent coronary computed tomography angiography (CCTA) during the same period (control group). Clinical and imaging data were analyzed. In the ACS group, plaques were classified as culprit or non-culprit plaques. Based on the number of high-risk features, plaques were further categorized as non-high-risk or high-risk. FAI surrounding plaques was measured using predefined default (-190 to -30 HU) and wide (-190 to 20 HU) attenuation thresholds. Student’s t-test, one-way ANOVA, and chi-square test were used to compare FAI values of plaques with different characteristics and degrees of stenosis between and within groups; the plaque characteristics, stenosis severity, and FAI among culprit plaques, non-culprit plaques, and control group plaques; the high-risk features between culprit and non-culprit plaques; and the FAI values between high-risk and non-high-risk plaques. Multivariable logistic regression analysis was performed to identify independent predictors of culprit plaques. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of individual and combined factors for culprit plaques. The DeLong test was used to compare the differences in the area under the curve (AUC) among individual and combined factors. Results The FAI measured with the wide threshold was significantly higher than that measured with the default threshold for culprit plaques, non-culprit plaques, and control group plaques (all P<0.05). Under both thresholds, the FAI of culprit plaques was significantly greater than that of non-culprit plaques and control plaques (all P<0.05). Among the culprit plaques, 64% were classified as high-risk plaques, and these also showed high proportions of mixed plaque morphology, severe stenosis, and occlusion (52%, 76%, and 12%, respectively). In the ACS group, the FAI surrounding calcified plaques was lower than that surrounding non-calcified and mixed plaques (P<0.05). The FAI was significantly higher around plaques causing severe stenosis or occlusion (P<0.05), and higher around high-risk plaques compared to non-high-risk plaques (P<0.05). Multivariable logistic regression analysis indicated that stenosis severity ≥ moderate, higher default threshold FAI, and a greater number of high-risk plaque features were independent predictors of culprit plaques. The combination of default threshold FAI, stenosis severity, and high-risk features yielded the highest predictive performance (AUC=0.981). DeLong test analysis showed that the AUCs of models combining default threshold FAI with other factors were significantly higher than those of any single factor alone (all P<0.05). Conclusion The FAI surrounding ACS plaques can partially reflect plaque inflammation and vulnerability. Combining default threshold FAI with stenosis severity and high-risk features improves diagnostic performance in identifying culprit plaques.

Objective To evaluate the accuracy of fat quantification and radiation dose levels of photon-counting detector (PCD) CT under various scanning protocols. Methods Phantoms with 11 different fat concentrations [true fat fractions (FF): 0%, 2.5%, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, and 100%] were constructed. Scans were performed using PCD-CT with 3 scanning modes (axial scan, conventional helical scan, and high-pitch helical scan), 4 tube voltages [90 kV, 120 kV, 140 kV, and tin-filtered (Sn) 100 kV], and 3 image quality (IQ) levels (20, 40, and 80), resulting in 36 scanning protocols. CT dose index (CTDI_vol) for each protocol was recorded. Using the conventional helical scan with 120 kV and IQ 80 as the reference, CT values from all protocols were converted to FF values via linear regression. Accuracy and consistency were assessed using intraclass correlation coefficients (ICC) and Bland-Altman analysis. FF derived from PCD-CT across protocols was compared using one-way repeated measures ANOVA. Results PCD-CT demonstrated excellent accuracy and consistency for FF across all protocols [ICC>0.9 (range: 0.929-0.998, P<0.017)]. The root mean square error (RMSE) between PCD-CT-derived FF and true FF ranged from 1.0% to 5.0%, with the lowest RMSE (1.0%) observed in the high-pitch helical scan at 120 kV and IQ 20. The conventional helical scans at 120 kV with IQ 20 and IQ 80 had the lowest bias values (mean biases of 1.19% and 1.23%, respectively). Radiation doses across the 36 protocols ranged from 0.09 to 1.45 mGy. Conclusion PCD-CT achieves high accuracy in fat quantification across various scanning protocols and has potential for accurate fat quantification at ultra-low radiation doses.

Preoperative prediction of the efficacy of neoadjuvant immunotherapy (NIT) in non-small cell lung cancer (NSCLC) helps identify patients who are likely to benefit, reduce the risk of postoperative recurrence and metastasis, and improve prognosis. Radiomics and deep learning can be used to explore imaging biomarkers for predicting NIT efficacy in NSCLC. Radiomics, through global feature analysis or habitat analysis methods, can effectively quantify the temporal and spatial heterogeneity of tumors, providing a quantitative basis for efficacy prediction. Deep learning, on the other hand, adaptively extracts deep imaging features to evaluate treatment response. This review summarizes recent research progress in radiomics and deep learning technologies for predicting NIT efficacy in NSCLC patients, and discusses the associated technical challenges and corresponding solutions.

Chronic obstructive pulmonary disease (COPD) is a common and preventable condition, and early diagnosis and intervention are crucial for improving patient outcomes. Pulmonary CT imaging enables quantitative analysis of lung density, airways, vessels, and body composition, revealing COPD phenotypes and identifying comorbidities. This approach aids in assessing the severity of COPD and facilitating early intervention. This review summarizes the research progress on quantitative chest CT analysis in COPD.

Body fat percentage serves as a crucial indicator for measuring an individual’s body fat content. Ultrasound, as a safe and non-invasive examination method, not only enables visual detection of fat layer thickness and effective differentiation between subcutaneous and visceral fat, but also allows assessment of body fat percentage through quantitative measurements of fat thickness at multiple sites. Notably, appropriate selection of measurement sites is particularly beneficial for improving the accuracy of body fat percentage estimation. This review summarizes the potential value, reproducibility, and application ultrasound in measuring subcutaneous and visceral fat for body fat percentage assessment.

Early diagnosis and accurate assessment of kidney damage are crucial for the treatment and prognosis of chronic kidney disease (CKD). Radiomics can deeply mine information from medical images and extract a large number of quantitative features that are not recognizable by the human eye, thereby constructing models for the diagnosis and staging of CKD, as well as evaluating kidney function and the degree of renal fibrosis. This paper reviews the research progress of radiomics based on ultrasound, MRI, and CT in the diagnosis and evaluation of CKD.

Objective To explore the value of a machine learning model incorporating primary tumor and peritumoral radiomics features for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer. Methods Clinical and imaging data of 148 patients with pathologically confirmed gastric cancer were retrospectively collected. Based on pathological results, patients were divided into an LVI-positive group (79 cases) and an LVI-negative group (69 cases). Patients were randomly divided into a training set (103 cases) and a test set (45 cases) in a 7∶3 ratio. Radiomic features were extracted from the primary tumor and peritumoral regions. The least absolute shrinkage and selection operator (LASSO) method was used to select optimal radiomic features, and the radiomics score (Rad-score) was calculated. The clinical features with statistically significant differences between the two groups were combined with Rad-score for multivariate logistic regression analysis to select variables for constructing a machine learning model. Seven machine learning algorithms, including logistic regression (LR), extreme gradient boosting (XGBoost), random forest (RF), Gaussian naive Bayes (GNB), support vector machine (SVM), light gradient boosting machine (LightGBM), and K-nearest neighbors (KNN), were used to construct clinical-radiomics models. The performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis. Calibration curves and decision curve analysis (DCA) were used to assess the calibration degree and clinical net benefit of the models, respectively. The SHapley Additive exPlanations (SHAP) method was employed to provide visual interpretation of the predictive model. Results In the training set, all seven machine learning models achieved an AUC greater than 0.650, with the RF model achieving the highest AUC (0.858), sensitivity (0.895), and accuracy (0.776). The calibration curve indicated that the RF model had the lowest Brier score (0.153), demonstrating the best predictive accuracy. DCA revealed that the RF model provided the highest net clinical benefit when the risk threshold ranged from 0.30 to 0.70. In the test set, the RF model maintained stable diagnostic performance, achieving an AUC of 0.821. SHAP analysis identified key factors associated with LVI risk in gastric cancer patients and provided visual interpretation for individual predictions. Conclusion The RF model, integrating primary tumor and peritumoral radiomic features with clinical factors, holds significant value for preoperative prediction of LVI status in gastric cancer patients.

Objective To explore the feasibility of radiomics models based on contrast-enhanced computed tomography (CECT) in distinguishing cephalic para-carcinoma tissues and resection margin in esophageal squamous cell carcinoma (ESCC) following neoadjuvant chemotherapy and immunotherapy (NACI). Methods This retrospective study included 188 pathologically confirmed ESCC patients who underwent NACI, recruited from two medical centers. A total of 138 patients from Center A were randomly divided into a training set (97 cases) and an internal validation set (41 cases) at a 7∶3 ratio, while 50 patients from Center B served as an external validation set. Using an open-source software 3D-Slicer, four regions of interest (ROIs) representing cephalic para-carcinoma tissues (P1, P2, P3, and P4) at 1 cm, 2 cm, 3 cm, and 4 cm above the tumor margin, respectively, and one ROI for resection margin tissue (P5, 5 cm above the tumor) were delineated on CECT images. Radiomics features were extracted using the Pyradiomics package. The radiomics features obtained from four cephalic para-carcinoma tissues were individually paired with those of resection margin tissue to differentiate between them, which were designated as groups P1, P2, P3, and P4, respectively. Univariate analysis and the least absolute shrinkage and selection operator (LASSO) method were employed to select optimal radiomics features in the training sets, and logistic regression models were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the discriminatory performance of the radiomics models. Results The AUCs of the P1 model in the training, internal validation, and external validation sets were 0.831, 0.820, and 0.787, respectively. The AUCs of the P2 model were 0.809, 0.797, and 0.769, respectively. Both the P1 and P2 models demonstrated good discriminatory performance (AUC>0.76), with the P1 model achieving higher AUC values than the P2 model in all datasets. Conclusion The CECT-based radiomics model demonstrates high efficacy in distinguishing cephalad peritumoral (P1 and P2) and resection margin tissues in ESCC following NACI.

Magnetic resonance imaging (MRI), as a tool for evaluating multi-organ impairment due to liver cirrhosis, enables precise identification of microstructural abnormalities and effective evaluation of organ function. When combined with clinical and laboratory indicators, MRI facilitates monitoring of disease progression and treatment efficacy, supporting the development of diagnostic and therapeutic strategies. This article reviews the current applications of MRI in diagnosing and managing multi-organ damage resulting from liver cirrhosis, highlighting advancements and value of both conventional and functional MRI in monitoring disease, evaluating treatment effectiveness, and predicting long-term outcomes for hepatic encephalopathy, cirrhosis-related injury, and hepatocardiac syndrome. Finally, it discusses future directions for development.

Coronary computed tomography angiography (CCTA) is the preferred non-invasive imaging modality for the diagnosis, clinical treatment decision-making, therapeutic efficacy assessment, and prognosis prediction of coronary artery disease (CAD). CCTA can be used not only to evaluate plaque characteristics and the degree of luminal stenosis but also to provide quantitative parameters reflecting coronary flow reserve, peri-coronary fat inflammation, left ventricular myocardial strain, myocardial fibrosis, and myocardial perfusion. This review summarizes the application of these quantitative derived parameters in CAD.

Objective To explore the feasibility of training a deep learning model for fully automated adrenal segmentation on non-contrast CT images. Methods The images and clinical data of 1 200 patients who underwent non-contrast adrenal CT scan were retrospectively collected. Using a 5-fold cross-validation method, patients were divided into a training set (960 cases) and an internal test set (240 cases) at an 8∶2 ratio. Additionally, 81 cases who underwent adrenal CT scans were collected as an independent test set. Both 2D nnU-Net and 3D nnU-Net segmentation models were constructed based on the nnU-Net framework. Clinical and CT imaging features were compared between the two groups using the Mann-Whitney U test and chi-square test. The model’s segmentation performance was objectively evaluated using the Dice coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), recall, and precision from the internal testing set and independent testing set. Two radiologists subjectively evaluated the 3D nnU-Net segmentation results on the independent test set. Results No statistically significant differences were observed in general characteristics between the training set+internal test set and independent test set (all P>0.05). Both 2D and 3D nnU-Net models achieved high segmentation performance for the left and right adrenal glands on the internal and independent test sets. Compared to the 2D nnU-Net model, the 3D nnU-Net model demonstrated higher DSC and precision, lower HD and ASSD, and similar or higher recall. The segmentation results of the 3D nnU-Net were closer to manual annotations compared to the 2D nnU-Net model. Subjective evaluation by two radiologists on the independent test set revealed 62.96% satisfactory and 37.04% unsatisfactory segmentation outcomes for the 3D nnU-Net. Conclusion The deep learning-based adrenal segmentation mode is feasible for automatic adrenal segmentation on non-contrast CT images.

Breast cancer is the most common malignancy in women, with diverse subtypes and treatment options, making early diagnosis and classification critical. Dual-energy CT (DECT) enhances material differentiation by utilizing two distinct X-ray energy levels. The iodine density maps generated through post-processing provide clear visualization of breast cancer lesions and the extent of internal duct expansion. Additionally, DECT yields multiple parameters that aid in molecular typing, as well as in predicting metastasis, prognosis, and treatment efficacy for breast cancer. This article reviews the application and research progress of DECT in breast cancer.

Objective To investigate differences in white matter microstructure between patients with neuropsy-chiatric systemic lupus erythematosus (NPSLE) and those with non-neuropsychiatric SLE (Non-NPSLE) using tract-based spatial statistics (TBSS). Methods A total of 34 NPSLE patients and 32 Non-NPSLE patients were prospectively enrolled, along with 33 healthy controls (HC) during the same period. All participants underwent brain diffusion tensor imaging (DTI). TBSS was used to compare white matter microstructural differences among the three groups. Fractional anisotropy (FA) values were compared using one-way ANOVA, with post hoc analyses conducted for pairwise group comparisons. Partial correlation analyses assessed the relationships between FA values of significantly different clusters and neuropsychological scores or clinical indicators, as well as the correlations between neuropsychological scores and clinical indicators. Results Five clusters showed significant FA differences among the three groups (P<0.05, FWE-corrected). Post hoc analysis revealed that two clusters in both the Non-NPSLE and NPSLE groups had lower FA values than the HC group, and one cluster in the NPSLE group had a lower FA value than the Non-NPSLE group (P<0.05, FWE-corrected), indicating more extensive white matter involvement in NPSLE. FA reductions in SLE patients were primarily located in the corpus callosum and corona radiata. Correlation analysis showed that FA values of the significant clusters in pairwise comparisons were positively correlated with IgM levels (P<0.05). In the NPSLE group, HADS-D scores were negatively correlated with C4 levels (r=-0.354, P=0.047), while in the Non-NPSLE group, MoCA scores were negatively correlated with ESR (r=-0.424, P=0.019). Conclusion NPSLE patients exhibit more extensive white matter microstructural damage compared to Non-NPSLE patients. The FA values of some differential clusters correlate with clinical indicators, suggesting that these changes may serve as important imaging biomarkers for detecting disease activity or neuropsychiatric involvement in SLE.

Objective This study aims to evaluate the feasibility of low-dose chest CT (LD-CT) using photon-counting detector CT (PCD-CT) for the detection, quantification, and risk stratification of coronary artery calcium. Methods A retrospective analysis was conducted on 63 patients with coronary artery calcium who underwent standard calcium scoring CT (ECG-CT) and LD-CT with PCD-CT, a total of 189 vessels involved. Twenty-nine patients were divided into high heart rate group (heart rate >75 beats/min) and 34 patients into low heart rate group (heart rate <75 beats/min). ECG-CT was performed using a prospective ECG-gated 120 kVp scan, while LD-CT utilized non-ECG-gated high-pitch combined with tin filtration at 100 kVp (Sn100 kVp) settings. The Agatston score was used for quantifying coronary artery calcium. Using ECG-CT as the reference standard, the sensitivity, specificity, and accuracy of LD-CT in detecting coronary artery calcification were calculated at both patient and vessel levels, as well as at high and low heart rates. The correlation and agreement between LD-CT and ECG-CT in assessing AS were analyzed by the Spearman correlation coefficient (r) and Bland-Altman method (bias: 95% limits of agreement). The agreement of coronary artery calcification risk stratification was assessed by the Weighted Kappa analysis. The difference in effective radiation doses between LD-CT and ECG-CT was compared using the paired t-test. Results ECG-CT detected coronary artery calcification in 130 vessels across all 63 patients. For all patients, LD-CT showed high accuracy(100%) in detecting coronary artery calcification. A strong correlation (r=0.95~0.99，P<0.05) and consistency (-9.7：-125/105.7) were observed between the Agatston scores obtained from both methods. The bias in the left anterior descending artery (LAD) (0.1：-102.8/102.9) was smaller than the left circumflex artery (LCX) (-11.5：-86.9/63.9) and the right coronary artery(RCA) (-8.1:-81.2/65.1). The bias in the low heart rate group (-3.3： -73.4/66.5) was smaller than in the high heart rate group(-18.3：-175.3/138.6). Strong agreement was found in risk stratification based on Agatston scores (kappa=0.963). The effective radiation dose of LD-CT (0.48±0.9 mSv) was 37% (P<0.001) lower than that of ECG-CT (0.77±0.16 mSv). Conclusion LD-CT using PCD-CT not only demonstrated better accuracy in the detection, quantification, and risk stratification of coronary artery calcium but also significantly reduced effective radiation dose.

Objective To compare the dosimetry and efficacy differences among Hyperarc, volume modulated arc therapy (VMAT), intensity modulated radiation therapy (IMRT), and three-dimensional conformal radiation therapy (3DCRT) for the treatment of multiple brain metastases. Methods A total of 168 patients with multiple brain metastases were prospectively selected and randomly divided into 4 groups (n=42), each receiving treatment plans designed using Hyperarc, VMAT, IMRT, or 3DCRT. One-way ANOVA, Kruskal Wallis test, and chi-square test were used to compare the clinical data and treatment efficacy, as well as the dosimetric parameters of the planning target volume [homogeneity index (HI), conformity index (CI), gradient index (GI)], organ at risk dose [maximum dose (Dmax) and average dose (Dmean) to bilateral crystals and brainstem], and monitor units and beam delivery time, among the 4 groups. Results The treatment efficacy of the Hyperarc group was significantly higher than that of the VMAT, IMRT, and 3DCRT groups (P<0.05). Compared with the other three groups, the Hyperarc group had lower HI, GI, and Dmax/Dmean for the bilateral lenses and brainstem, while Dmean and CI of target volume were higher (all P<0.05). The VMAT and IMRT groups exhibited lower HI, GI, and Dmax/Dmean for the bilateral lenses and brainstem compared to the 3DCRT group (all P<0.05), while their target volume Dmean and CI were higher (P<0.05). The Hyperarc group had fewer monitor units and a shorter beam-on time than the VMAT and IMRT groups, but higher than the 3DCRT group (all P<0.05). Conclusion In the radiotherapy of multiple brain metastases, Hyperarc technology demonstrates advantages in target volume dosimetric distribution and organ-at-risk protection.

Skeletal muscle, as an important component of the skeletal-muscular system, exhibits unique anatomical and structural characteristics. Pathophysiological changes in skeletal muscle are critical to movement, treatment approaches, and prognosis of skeletal muscle diseases. This review provides an overview of MRI-based quantitative parameters used to assess skeletal muscle morphology and histology (such as cross-sectional area, fat content, inflammatory edema status, and muscle fiber structure types), their assessment methods, and clinical applications, as well as the correlation between skeletal muscle health or disease states and MRI-derived quantitative parameters. Additionally, the review highlights recent advances in multimodal MRI for the quantitative evaluation of skeletal muscle structure and function, offering insights into potential clinical applications.

Imaging assessment plays a crucial role in the follow-up of patients after radical surgery for colorectal cancer. Fibroblast activation protein inhibitor (FAPI), as an emerging radionuclide tracer, is not affected by glucose metabolism and offers a higher tumor-to-background ratio, making it more effective in detecting recurrent lesions. It is particularly advantageous in the detection of non-FDG-avid tumors, peritoneal metastases, and small lesions. This review summarizes the advances in the application of FAPI PET/CT for assessing recurrence and metastasis after radical surgery for colorectal cancer.

Vascular lesions of the head and neck are one of the major contributing factors to serious health issues such as stroke, with pathological progression closely linked to hemodynamic abnormalities. Computational fluid dynamics (CFD), through patient-specific three-dimensional vascular modeling, enables the quantitative analysis of geometric parameters and hemodynamic characteristics. This approach elucidates the pathophysiological mechanisms of carotid atherosclerosis and intracranial aneurysms, playing a crucial role in predicting disease progression and guiding therapeutic strategies. This review systematically summarizes recent advancements in CFD applications for cerebrovascular pathologies of the head and neck region.

Neoadjuvant chemotherapy (NAC) can not only reduce the stage of breast cancer but also enable some tumor lesions and axillary lymph nodes to achieve pathological complete response (pCR). Accurate preoperative imaging assessment of axillary lymph node status after NAC in breast cancer patients can help avoid excessive surgical intervention and guide the development of individualized treatment plans. This review summarizes recent progress in the use of imaging methods such as ultrasound, MRI, CT, and PET/CT to evaluate axillary lymph node pCR after NAC.

Objective To establish a logistic regression model based on CT and MRI features of multilocular hepatic cysts and mucinous cystic tumors (MCN), and analyze its value in differential diagnosis between the two entities. Methods A retrospective analysis was conducted on CT and MRI data from 65 cases of multilocular hepatic cystic lesions, including 13 males and 52 females. According to surgical pathology results, the cases were divided into hepatic cyst lesions (39 cases) and hepatic MCN lesions (26 cases). Chi-square tests were used to compare imaging findings between the two groups. Statistically significant CT and MRI features were further analyzed using multivariate logistic regression to construct a binary logistic regression model. The predictive performance of the model was evaluated using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC), sensitivity, specificity, and accuracy were calculated. Results Significant differences were observed between hepatic cysts and hepatic MCNs in terms of the number of hepatic cystic lesions, the appearance of cyst wall and septa, nodular protrusions of cyst wall or septa, the thickness of solid components greater than 10 mm, types of septa, septa location, and the relationship between septa and cyst walls between hepatic cysts and hepatic MCNs (all P<0.05). Multivariate logistic regression analysis identified that septal type, and the relationship between septa and cyst walls as independent predictive factors (P<0.05). A logistic regression model constructed using these two factors achieved higher diagnostic performance (AUC=0.871) compared to using septal type (AUC=0.699) or the relationship between septa and cyst walls (AUC=0.795) alone. Conclusion A logistic regression model incorporating septal type, and the relationship between septa and cyst walls can effectively distinguish multilocular hepatic cysts from hepatic MCN, improving the preoperative imaging diagnostic accuracy for these lesions.

Heart failure with preserved ejection fraction (HFpEF) is a condition characterized by high heterogeneity and complex etiologies. Among its various underlying mechanisms, myocardial fibrosis is a key pathophysiological factor contributing to cardiac dysfunction in HFpEF patients. This article reviews the progress in applying various cardiac magnetic resonance (CMR) techniques, including delayed gadolinium enhancement (LGE), T₁ mapping, extracellular volume (ECV), and CMR feature tracking (CMR-FT), to assess myocardial fibrosis in HFpEF. It highlights the role of these techniques in evaluating the distribution and severity of myocardial fibrosis, as well as their utility in risk stratification and prognostic assessment. Additionally, the article explores the future potential of artificial intelligence in enhancing CMR-based evaluations.